We investigate the potential of adaptive blind equalizers based on variational inference for carrier recovery in optical communications. These equalizers are based on a low-complexity approximation of maximum likelihood channel estimation. We generalize the concept of variational autoencoder (VAE) equalizers to higher order modulation formats encompassing probabilistic constellation shaping (PCS), ubiquitous in optical communications, oversampling at the receiver, and dual-polarization transmission. Besides black-box equalizers based on convolutional neural networks, we propose a model-based equalizer based on a linear butterfly filter and train the filter coefficients using the variational inference paradigm. As a byproduct, the VAE also provides a reliable channel estimation. We analyze the VAE in terms of performance and flexibility over a classical additive white Gaussian noise (AWGN) channel with inter-symbol interference (ISI) and over a dispersive linear optical dual-polarization channel. We show that it can extend the application range of blind adaptive equalizers by outperforming the state-of-the-art constant-modulus algorithm (CMA) for PCS for both fixed but also time-varying channels. The evaluation is accompanied with a hyperparameter analysis.

翻译：我们根据光学通信中载体恢复的变异推断,调查适应性盲均分器的潜力。这些均分器以最大可能性频道估计的低复度近似值为基础。我们将变式自动电解器(VAE)等同器的概念推广到更高排序的调制格式,包括概率型星形成型(PCS)、光学通信中无处不在、接收器过度采样和双极传输。除了基于动态神经网络的黑箱平衡器外,我们还提议以线形蝴蝶过滤器为基础的模型平衡器,并利用变异推断模式培训过滤系数。作为副产品,VAE还提供可靠的频道估计。我们从功能和灵活性的角度分析VAE,它涉及典型添加白高地噪音的添加剂干扰(ISI)和分辨性线性线性双极化频道。我们表明,它可以通过超越州-CMA双轨的时空分析来扩大盲人适应性平衡器的应用范围。我们显示,对州-CMA双轨的常态算法进行超时空分析。